Built on

A. F. Martin and C. S. Greenberg, “NIST 2008 speaker recognition evaluation: Performance across telephone and room microphone channels,” in
2009
Earlier work this paper cites.
——, “The NIST 2010 speaker recognition evaluation,” in
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, and Others, “The kaldi speech recognition toolkit,” in
2011
Earlier work this paper cites.
D. Snyder, G. Chen, and D. Povey, “Musan: A music, speech, and noise corpus,”
Original
2015
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, and M. Yang, “Large-margin softmax loss for convolutional neural networks,” in
2016
Earlier work this paper cites.
C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y. Cao, A. Kannan, and Z. Zhu, “Deep speaker: an end-to-end neural speaker embedding system,”
Original
2017
Earlier work this paper cites.
C. Zhang and K. Koishida, “End-to-end text-independent speaker verification with triplet loss on short utterances,” in
2017
Earlier work this paper cites.
A. Hermans, L. Beyer, and B. Leibe, “In defense of the triplet loss for person re-identification,”
Original
2017
Earlier work this paper cites.
C.-Y. Wu, R. Manmatha, A. J. Smola, and P. Krahenbuhl, “Sampling matters in deep embedding learning,” in
2017
Earlier work this paper cites.
R. Ranjan, C. D. Castillo, and R. Chellappa, “L2-constrained softmax loss for discriminative face verification,”
Original
2017
Earlier work this paper cites.
F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “Normface: l2 hypersphere embedding for face verification,” in
2017
Earlier work this paper cites.

Similar

W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in
2017
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in
2017
Cited alongside, same era.
T. Ko, V. Peddinti, D. Povey, M. L. Seltzer, and S. Khudanpur, “A study on data augmentation of reverberant speech for robust speech recognition,” in
2017
Cited alongside, same era.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in
2018
Cited alongside, same era.
R. Ji, X. Cai, and B. Xu, “An end-to-end text-independent speaker identification system on short utterances,” in
2018
Cited alongside, same era.

Then

J. Deng, J. Guo, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,”
Original
2018
Later among the works it cites.
Z. Huang, S. Wang, and K. Yu, “Angular softmax for short-duration text-independent speaker verification,” in
2018
Later among the works it cites.
Y. Li, F. Gao, Z. Ou, and J. Sun, “Angular softmax loss for end-to-end speaker verification,”
Original
2018
Later among the works it cites.
G. Bhattacharya, J. Alam, and P. Kenny, “Adapting end-to-end neural speaker verification to new languages and recording conditions with adversarial training,”
Original
2018
Later among the works it cites.
M. Hajibabaei and D. Dai, “Unified hypersphere embedding for speaker recognition,”
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2019

Large Margin Softmax Loss for Speaker Verification

Liu, Yi, He, Liang, Liu, Jia

Understand

In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small.

A variety of loss functions have been proposed to achieve this goal.
In this paper, we investigate the large margin softmax loss with different configurations in speaker verification.
Ring loss and minimum hyperspherical energy criterion are introduced to further improve the performance.

Built on

A. F. Martin and C. S. Greenberg, “NIST 2008 speaker recognition evaluation: Performance across telephone and room microphone channels,” in
2009
Earlier work this paper cites.
——, “The NIST 2010 speaker recognition evaluation,” in
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, and Others, “The kaldi speech recognition toolkit,” in
2011
Earlier work this paper cites.
D. Snyder, G. Chen, and D. Povey, “Musan: A music, speech, and noise corpus,”
Original
2015
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, and M. Yang, “Large-margin softmax loss for convolutional neural networks,” in
2016
Earlier work this paper cites.
C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y. Cao, A. Kannan, and Z. Zhu, “Deep speaker: an end-to-end neural speaker embedding system,”
Original
2017
Earlier work this paper cites.
C. Zhang and K. Koishida, “End-to-end text-independent speaker verification with triplet loss on short utterances,” in
2017
Earlier work this paper cites.
A. Hermans, L. Beyer, and B. Leibe, “In defense of the triplet loss for person re-identification,”
Original
2017
Earlier work this paper cites.
C.-Y. Wu, R. Manmatha, A. J. Smola, and P. Krahenbuhl, “Sampling matters in deep embedding learning,” in
2017
Earlier work this paper cites.
R. Ranjan, C. D. Castillo, and R. Chellappa, “L2-constrained softmax loss for discriminative face verification,”
Original
2017
Earlier work this paper cites.
F. Wang, X. Xiang, J. Cheng, and A. L. Yuille, “Normface: l2 hypersphere embedding for face verification,” in
2017
Earlier work this paper cites.

Similar

W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in
2017
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in
2017
Cited alongside, same era.
T. Ko, V. Peddinti, D. Povey, M. L. Seltzer, and S. Khudanpur, “A study on data augmentation of reverberant speech for robust speech recognition,” in
2017
Cited alongside, same era.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in
2018
Cited alongside, same era.
R. Ji, X. Cai, and B. Xu, “An end-to-end text-independent speaker identification system on short utterances,” in
2018
Cited alongside, same era.

Then

J. Deng, J. Guo, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,”
Original
2018
Later among the works it cites.
Z. Huang, S. Wang, and K. Yu, “Angular softmax for short-duration text-independent speaker verification,” in
2018
Later among the works it cites.
Y. Li, F. Gao, Z. Ou, and J. Sun, “Angular softmax loss for end-to-end speaker verification,”
Original
2018
Later among the works it cites.
G. Bhattacharya, J. Alam, and P. Kenny, “Adapting end-to-end neural speaker verification to new languages and recording conditions with adversarial training,”
Original
2018
Later among the works it cites.
M. Hajibabaei and D. Dai, “Unified hypersphere embedding for speaker recognition,”
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

W. Wan, Y. Zhong, T. Li, and J. Chen, “Rethinking feature distribution for loss functions in image classification,” in

2018

Cited alongside, same era.

N. Li, D. Tuo, D. Su, Z. Li, and D. Yu, “Deep discriminative embeddings for duration robust speaker verification,” in

2018

Cited alongside, same era.

S. Yadav and A. Rai, “Learning discriminative features for speaker identification and verification,” in

2018

Cited alongside, same era.

L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in

2018

Cited alongside, same era.

F. Wang, J. Cheng, W. Liu, and H. Liu, “Additive margin softmax for face verification,”

2018

Cited alongside, same era.

H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” in

2018

Cited alongside, same era.

2018